Neural Network and Self-organizing Maps

2011 ◽  
pp. 87-100
Author(s):  
Yong Yin ◽  
Ikou Kaku ◽  
Jiafu Tang ◽  
JianMing Zhu
Keyword(s):  
Neural Network ◽  
Self Organizing Maps ◽  
Self Organizing

Machine Learning: Automated Knowledge Acquisition Based on Unsupervised Neural Network and Expert System Paradigms

2005 ◽  
Vol 9 (6) ◽  
pp. 693-697 ◽  
Author(s):  
Nazar Elfadil ◽  
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Knowledge Acquisition ◽  
Training Data ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Data Set ◽  
Unsupervised Neural Network ◽  
Automated Knowledge ◽  
Self Organizing

Self-organizing maps are unsupervised neural network models that lend themselves to the cluster analysis of high-dimensional input data. Interpreting a trained map is difficult because features responsible for specific cluster assignment are not evident from resulting map representation. This paper presents an approach to automated knowledge acquisition using Kohonen's self-organizing maps and k-means clustering. To demonstrate the architecture and validation, a data set representing animal world has been used as the training data set. The verification of the produced knowledge base is done by using conventional expert system.

scholarly journals Using self-organizing maps for determination of soil fertility (case study: Shiraz plain)

2018 ◽  
Vol 13 (No. 1) ◽  
pp. 11-17 ◽  
Author(s):  
M. Mokarram ◽  
M. Najafi-Ghiri ◽  
A.R. Zarei
Keyword(s):  
Neural Network ◽  
Soil Fertility ◽  
Network Models ◽  
The Other ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Soil Features ◽  
Close Relationship ◽  
Artificial Neural Network Ann ◽  
Self Organizing

Soil fertility refers to the ability of a soil to supply plant nutrients. Naturally, micro and macro elements are made available to plants by breakdown of the mineral and organic materials in the soil. Artificial neural network (ANN) provides deeper understanding of human cognitive capabilities. Among various methods of ANN and learning an algorithm, self-organizing maps (SOM) are one of the most popular neural network models. The aim of this study was to classify the factors influencing soil fertility in Shiraz plain, southern Iran. The relationships among soil features were studied using the SOM in which, according to qualitative data, the clustering tendency of soil fertility was investigated using seven parameters (N, P, K, Fe, Zn, Mn, and Cu). The results showed that for soil fertility there is a close relationship between P and N, and also between P and Zn. The other parameters, such as K, Fe, Mn, and Cu, are not mutually related. The results showed that there are six clusters for soil fertility and also that group 1 soils are more fertile than the other.

Ketepatan Klasifikasi Penerima Beasiswa STMIK STIKOM Bali dengan Hybrid Self Organizing Maps dan Algoritma K-Mean

Jurnal Varian ◽  
2018 ◽  
Vol 2 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Ni Putu Nanik Hendayanti ◽  
Gusti Ayu Made Arna Putri ◽  
Maulida Nurhidayati
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Self Organizing Maps ◽  
Cluster Group ◽  
Artificial Neural ◽  
Clustering Data ◽  
Self Organizing

Data Mining adalah penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data yang sangat besar. Salah satu teknik yang dikenal dalam Data Mining yaitu clustering. Pengertian clustering dalam Data Mining adalah pengelompokan sejumlah data atau objek ke dalam cluster (group) sehingga setiap di lama cluster tersebut akan berisi data yang semirip mungkin dan berbeda dengan objek dalam cluster yang lain. Salah satu metode klasifiaksi atau clustering adalah Self Organizing Maps (SOM). SOM merupakan metode artificial neural network yang digunakan untuk mengelompokkan (clustering) data berdasarkan karakteristik/fitur-fitur data. Metode pengelompokan yang menggunakan konsep jarak dan memiliki karakteristik yang hampir sama dengan SOM yaitu metode K-means. Penelitian ini bertujuan untuk mengembangkan suatu metode yang merupakan hybrid dari SOM dan K-means yang digunakan untuk menentukan ketepatan suatu klasifikasi. Sebelum diujikan pada data asli, metode hybrid SOM dan K-Means diujikan lebih dulu pada data benchmark sehingga dapat diketahui berapa persen ketepan yang dihasilkan. Kemudian dilanjutkan dengan penerapan metode hybrid SOM dan K-means pada data penerimaan beasiswa di STMIK STIKOM Bali. Penelitian ini bertujuan untuk menentukan ketepatan klasifikasi penerima beasiswa STMIK STIKOM Bali dengan metode hybrid SOM dan K-means. Hasil penelitian menunjukkan bahwa metode Kmeans dan SOM memberikan hasil yang sama yang akibatnya metode SOM-Kmeans juga memberikan hasil yang sama. Alasannya, metode SOM-Kmeans menggunakan nilai centroid dari hasil SOM, dan hasil yang diperoleh pada metode Kmean memiliki hasil yang sama dengan SOM akibatnya metode SOM-Kmeans menghasilkan hasil yang sama dengan kedua metode sebelumnya.

Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network

2019 ◽  
Vol 23 (1) ◽  
Author(s):  
J. M. Barrón Adame ◽  
M. S. Acosta Navarrete ◽  
J. Quintanilla Domínguez ◽  
R. Guzmán Cabrera ◽  
M. Cano Contreras ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Self Organizing Maps ◽  
Som Neural Network ◽  
Self Organizing

Pile Samples Selection Method Based on Self-Organizing Maps Neural Network

2012 ◽  
Vol 204-208 ◽  
pp. 41-44
Author(s):  
Si Si Liu ◽  
Yun Lei Fan
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Set ◽  
Self Organizing Maps ◽  
Bp Network ◽  
Samples Selection ◽  
Som Network ◽  
Validation Set ◽  
Self Organizing

One-dimensional self-organizing maps neural network (SOM) is used in pile samples selection, and the outcome can improve the accuracy of back-propagation neural network (BP) is proved. Firstly, 71 pile samples are divided into 5 groups according to SOM node weights. Each group is divided into training set, testing set, validation set to build 5 independent BP networks, called BP2. Secondly, 5 groups training set are merged into a new training set, similarly, a new test set and validation set to build another BP network, called BP1. At last, comparison of the performance of BP1 and BP2 show that using SOM network to select pile samples can build a BP network with better performance.

FLEXIBLE STRUCTURE MULTIPLE MODELING USING IRREGULAR SELF-ORGANIZING MAPS NEURAL NETWORK

2008 ◽  
Vol 18 (03) ◽  
pp. 233-256 ◽  
Author(s):  
ALIREZA FATEHI ◽  
KENICHI ABE
Keyword(s):  
Neural Network ◽  
Minimum Spanning Tree ◽  
Model Space ◽  
Self Organizing Map ◽  
Local Models ◽  
Tree Graph ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
The Neural Networks ◽  
Self Organizing

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.

Hybrid Intelligent Tutoring System With Didactic Transposition Of The Subjects Guided By Expert Knowlegdment And Self Organizing Maps Neural Network

2014 ◽  
Vol 12 (8) ◽  
pp. 1539-1544 ◽  
Author(s):  
Sirlon Diniz de Carvalho ◽  
Edna Lucia Flores ◽  
Francisco Ramos de Melo ◽  
Luiz Fernando Batista Loja ◽  
Milena Bueno Pereira Carneiro
Keyword(s):  
Neural Network ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Tutoring System ◽  
Self Organizing Maps ◽  
Didactic Transposition ◽  
Self Organizing
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